Multi-Stage Neural Supporting System for Time Domain Metal Detectors

In this work we propose an end-user supporting system for humanitarian demining tasks to semi-automatically classify signals of time domain metal detectors. Our multi-stage system consists of a first module to smooth the raw signals, followed by a neural feedforward net to classify the received signal´s decay curve of the localized object at each sensor position. The resulting output activities of this net are accumulated to spatial vectors, which are propagated to a second feedforward net. Its resulting output activities are visualized in a 3D-end user interface and may be analyzed by different signal processing routines to be sensitive to changing soils and environmental conditions.